Rank

Model

average

V1

V2

V4

IT

behavior

engineering

Deng2009-top1 v1

1 CORnet-S
Kubilius et al., 2018
.417 .294 .294 .242 .242 .581 .581 .423 .541 .305 .545 .545 .747 .747
2 vgg-19
Simonyan et al., 2014
.408 .347 .347 .341 .341 .610 .610 .248 .496 X .494 .494 .711 .711
3 resnet-50-robust
Santurkar et al., 2019
.408 .378 .378 .365 .365 .537 .537 .243 .486 X .515 .515
4 resnet-101_v1
He et al., 2015
.407 .266 .266 .341 .341 .590 .590 .274 .549 X .561 .561 .764 .764
5 vgg-16
Simonyan et al., 2014
.406 .355 .355 .336 .336 .620 .620 .259 .518 X .461 .461 .715 .715
6 resnet-152_v1
He et al., 2015
.405 .282 .282 .338 .338 .598 .598 .277 .553 X .533 .533 .768 .768
7 resnet-101_v2
He et al., 2015
.404 .274 .274 .332 .332 .599 .599 .263 .527 X .555 .555 .774 .774
8 resnet50-SIN_IN
Geirhos et al., 2019
.404 .282 .282 .324 .324 .599 .599 .276 .552 X .541 .541 .746 .746
9 densenet-169
Huang et al., 2016
.404 .281 .281 .322 .322 .601 .601 .274 .548 X .543 .543 .759 .759
10 densenet-201
Huang et al., 2016
.402 .277 .277 .325 .325 .599 .599 .273 .545 X .537 .537 .772 .772
11 resnet-50-pytorch
He et al., 2015
.399 .289 .289 .317 .317 .600 .600 .259 .518 X .528 .528 .752 .752
12 resnet-50_v1
He et al., 2015
.398 .274 .274 .317 .317 .594 .594 .278 .555 X .526 .526 .752 .752
13 resnet50-SIN_IN_IN
Geirhos et al., 2019
.397 .275 .275 .321 .321 .596 .596 .273 .545 X .523 .523 .767 .767
14 resnet-152_v2
He et al., 2015
.397 .274 .274 .326 .326 .591 .591 .266 .532 X .528 .528 .778 .778
15 resnet-50_v2
He et al., 2015
.396 .270 .270 .323 .323 .596 .596 .260 .520 X .531 .531 .756 .756
16 densenet-121
Huang et al., 2016
.396 .277 .277 .306 .306 .595 .595 .267 .533 X .535 .535 .745 .745
17 resnext101_32x32d_wsl
Mahajan et al., 2018
.396 .267 .267 .289 .289 .574 .574 .254 .507 X .594 .594 .854 .854
18 AT_efficientnet-b6
Xie et al., 2020
.395 .283 .283 .353 .353 .585 .585 .280 .559 X .474 .474
19 mobilenet_v1_1.0_160
Howard et al., 2017
.393 .290 .290 .332 .332 .588 .588 .275 .549 X .480 .480 .680 .680
20 resnext101_32x8d_wsl
Mahajan et al., 2018
.392 .271 .271 .312 .312 .586 .586 .241 .481 X .551 .551 .842 .842
21 inception_v2
Szegedy et al., 2015
.392 .284 .284 .313 .313 .587 .587 .270 .539 X .505 .505 .739 .739
22 resnet-18
He et al., 2015
.390 .274 .274 .302 .302 .583 .583 .266 .531 X .524 .524 .698 .698
23 mobilenet_v2_1.0_224
Howard et al., 2017
.389 .245 .245 .331 .331 .573 .573 .273 .546 X .521 .521 .718 .718
24 mobilenet_v2_0.75_224
Howard et al., 2017
.388 .236 .236 .316 .316 .586 .586 .268 .535 X .533 .533 .698 .698
25 efficientnet-b0
Tan et al., 2019
.387 .215 .215 .317 .317 .556 .556 .274 .547 X .573 .573
26 fixres_resnext101_32x48d_wsl
Touvron et al., 2019
.387 .246 .246 .288 .288 .582 .582 .257 .513 X .561 .561 .863 .863
26 resnext101_32x48d_wsl
Mahajan et al., 2018
.387 .246 .246 .288 .288 .582 .582 .257 .513 X .561 .561 .822 .822
28 AT_efficientnet-b8
Xie et al., 2020
.387 .294 .294 .333 .333 .569 .569 .272 .544 X .465 .465
29 mobilenet_v2_1.3_224
Howard et al., 2017
.386 .253 .253 .332 .332 .575 .575 .271 .543 X .500 .500 .744 .744
30 resnet50-SIN
Geirhos et al., 2019
.386 .300 .300 .333 .333 .580 .580 .267 .534 X .448 .448 .602 .602
31 pnasnet_large
Liu et al., 2017
.385 .264 .264 .305 .305 .578 .578 .263 .526 X .515 .515 .829 .829
32 AT_efficientnet-b7
Xie et al., 2020
.385 .276 .276 .308 .308 .583 .583 .281 .562 X .475 .475
33 mobilenet_v2_0.75_192
Howard et al., 2017
.384 .245 .245 .306 .306 .573 .573 .275 .550 X .524 .524 .687 .687
34 mobilenet_v2_1.4_224
Howard et al., 2017
.384 .257 .257 .321 .321 .566 .566 .277 .554 X .500 .500 .750 .750
35 inception_v1
Szegedy et al., 2014
.384 .259 .259 .311 .311 .589 .589 .244 .488 X .518 .518 .698 .698
36 xception
Chollet et al., 2016
.384 .245 .245 .306 .306 .610 .610 .249 .498 X .508 .508 .790 .790
37 AT_efficientnet-b4
Xie et al., 2020
.383 .246 .246 .339 .339 .549 .549 .279 .559 X .503 .503
38 mobilenet_v2_0.75_160
Howard et al., 2017
.383 .278 .278 .316 .316 .573 .573 .273 .547 X .473 .473 .664 .664
39 inception_v4
Szegedy et al., 2016
.382 .238 .238 .299 .299 .574 .574 .263 .526 X .537 .537 .802 .802
40 resnext101_32x16d_wsl
Mahajan et al., 2018
.382 .263 .263 .302 .302 .587 .587 .250 .499 X .509 .509 .851 .851
41 inception_resnet_v2
Szegedy et al., 2016
.381 .233 .233 .319 .319 .583 .583 .272 .543 X .499 .499 .804 .804
42 efficientnet-b6
Tan et al., 2019
.381 .263 .263 .295 .295 .563 .563 .271 .541 X .513 .513
43 efficientnet-b2
Tan et al., 2019
.380 .213 .213 .317 .317 .569 .569 .273 .547 X .526 .526
44 nasnet_large
Zoph et al., 2017
.380 .282 .282 .291 .291 .585 .585 .270 .541 X .470 .470 .827 .827
45 mobilenet_v1_1.0_224
Howard et al., 2017
.380 .223 .223 .341 .341 .560 .560 .273 .546 X .502 .502 .709 .709
46 efficientnet-b4
Tan et al., 2019
.379 .228 .228 .286 .286 .575 .575 .272 .543 X .535 .535
47 inception_v3
Szegedy et al., 2015
.379 .241 .241 .307 .307 .596 .596 .273 .545 X .477 .477 .780 .780
48 mobilenet_v2_1.0_192
Howard et al., 2017
.377 .216 .216 .322 .322 .572 .572 .273 .547 X .503 .503 .707 .707
49 mobilenet_v2_1.0_160
Howard et al., 2017
.376 .239 .239 .322 .322 .570 .570 .275 .550 X .472 .472 .688 .688
50 mobilenet_v2_0.5_192
Howard et al., 2017
.375 .263 .263 .329 .329 .566 .566 .264 .529 X .454 .454 .639 .639
51 mobilenet_v2_0.5_224
Howard et al., 2017
.372 .229 .229 .308 .308 .569 .569 .266 .533 X .488 .488 .654 .654
52 mobilenet_v1_0.75_224
Howard et al., 2017
.372 .223 .223 .336 .336 .558 .558 .267 .535 X .477 .477 .684 .684
53 AT_efficientnet-b2
Xie et al., 2020
.372 .248 .248 .295 .295 .563 .563 .275 .550 X .480 .480
54 resnet-34
He et al., 2015
.372 .230 .230 .286 .286 .560 .560 .237 .474 X .546 .546 .733 .733
55 AT_efficientnet-b0
Xie et al., 2020
.371 .238 .238 .334 .334 .570 .570 .267 .534 X .447 .447
56 mobilenet_v1_0.5_224
Howard et al., 2017
.370 .221 .221 .340 .340 .555 .555 .260 .521 X .474 .474 .633 .633
57 mobilenet_v1_1.0_192
Howard et al., 2017
.370 .235 .235 .329 .329 .548 .548 .271 .543 X .466 .466 .700 .700
58 mobilenet_v2_0.75_128
Howard et al., 2017
.369 .237 .237 .320 .320 .553 .553 .271 .541 X .464 .464 .632 .632
59 alexnet
Krizhevsky et al., 2012
.368 .316 .316 .353 .353 .550 .550 .254 .508 X .370 .370 .577 .577
60 mobilenet_v1_1.0_128
Howard et al., 2017
.368 .254 .254 .325 .325 .557 .557 .267 .535 X .437 .437 .652 .652
61 mobilenet_v1_0.75_128
Howard et al., 2017
.368 .267 .267 .330 .330 .564 .564 .252 .505 X .425 .425 .621 .621
62 mobilenet_v2_0.5_160
Howard et al., 2017
.368 .258 .258 .305 .305 .562 .562 .264 .528 X .448 .448 .610 .610
63 mobilenet_v2_1.0_128
Howard et al., 2017
.368 .252 .252 .303 .303 .569 .569 .267 .534 X .447 .447 .653 .653
64 mobilenet_v1_0.5_192
Howard et al., 2017
.367 .220 .220 .337 .337 .566 .566 .260 .520 X .454 .454 .617 .617
65 mobilenet_v1_0.75_192
Howard et al., 2017
.367 .229 .229 .339 .339 .549 .549 .267 .535 X .449 .449 .672 .672
66 mobilenet_v2_0.35_192
Howard et al., 2017
.366 .264 .264 .301 .301 .568 .568 .259 .518 X .437 .437 .582 .582
67 mobilenet_v2_1.0_96
Howard et al., 2017
.363 .256 .256 .332 .332 .530 .530 .257 .514 X .443 .443 .603 .603
68 mobilenet_v1_0.5_160
Howard et al., 2017
.361 .265 .265 .320 .320 .557 .557 .252 .503 X .410 .410 .591 .591
69 mobilenet_v2_0.35_160
Howard et al., 2017
.359 .269 .269 .292 .292 .554 .554 .259 .517 X .424 .424 .557 .557
70 mobilenet_v1_0.75_160
Howard et al., 2017
.359 .213 .213 .346 .346 .558 .558 .264 .529 X .413 .413 .653 .653
71 mobilenet_v2_0.35_224
Howard et al., 2017
.359 .215 .215 .296 .296 .554 .554 .253 .506 X .474 .474 .603 .603
72 mobilenet_v2_0.5_128
Howard et al., 2017
.358 .222 .222 .309 .309 .557 .557 .262 .525 X .440 .440 .577 .577
73 nasnet_mobile
Zoph et al., 2017
.357 .272 .272 .273 .273 .566 .566 .268 .536 X .406 .406 .740 .740
74 mobilenet_v2_0.75_96
Howard et al., 2017
.350 .208 .208 .305 .305 .527 .527 .258 .516 X .451 .451 .588 .588
75 squeezenet1_0
Iandola et al., 2016
.341 .304 .304 .320 .320 .591 .591 .229 .459 X .263 .263 .575 .575
76 mobilenet_v1_0.5_128
Howard et al., 2017
.341 .245 .245 .304 .304 .550 .550 .234 .467 X .373 .373 .563 .563
77 squeezenet1_1
Iandola et al., 2016
.336 .265 .265 .311 .311 .582 .582 .229 .457 X .291 .291 .575 .575
78 mobilenet_v2_0.35_128
Howard et al., 2017
.333 .245 .245 .289 .289 .530 .530 .235 .470 X .367 .367 .508 .508
79 mobilenet_v2_0.5_96
Howard et al., 2017
.331 .266 .266 .278 .278 .501 .501 .239 .479 X .370 .370 .512 .512
80 mobilenet_v1_0.25_224
Howard et al., 2017
.327 .231 .231 .296 .296 .538 .538 .240 .480 X .333 .333 .498 .498
81 mobilenet_v1_0.25_192
Howard et al., 2017
.323 .208 .208 .318 .318 .517 .517 .226 .451 X .344 .344 .477 .477
82 CORnet-Z
Kubilius et al., 2018
.322 .298 .298 .182 .182 .553 .553 .223 .447 X .356 .356 .470 .470
83 mobilenet_v1_0.25_160
Howard et al., 2017
.312 .198 .198 .293 .293 .509 .509 .229 .457 X .330 .330 .455 .455
84 bagnet9
Brendel et al., 2019
.307 .215 .215 .260 .260 .550 .550 .200 .401 X .307 .307 .260 .260
85 mobilenet_v2_0.35_96
Howard et al., 2017
.303 .183 .183 .249 .249 .501 .501 .230 .460 X .351 .351 .455 .455
86 mobilenet_v1_0.25_128
Howard et al., 2017
.302 .262 .262 .238 .238 .513 .513 .213 .425 X .286 .286 .415 .415
87 vggface
Parkhi et al., 2015
.301 .358 .358 .339 .339 .555 .555 .176 .351 X .078 .078
88 dcgan
None
.242 .316 .316 .226 .226 .432 .432 .214 .214 .023 .023
89 pixels
None
.030 .053 .053 .003 .003 .068 .068 .008 .015 X .020 .020
Model scores on brain benchmarks. Hover over model name to see layer commitments. The more green and bright a cell, the better the model's score. Scores are ceiled, hover the benchmark to see ceilings.

About

The Brain-Score platform aims to yield strong computational models of the ventral stream. We enable researchers to quickly get a sense of how their model scores against standardized brain benchmarks on multiple dimensions and facilitate comparisons to other state-of-the-art models. At the same time, new brain data can quickly be tested against a wide range of models to determine how well existing models explain the data.

Brain-Score is organized by the Brain-Score team in collaboration with researchers and labs worldwide. We are working towards an easy-to-use platform where a model can easily be submitted to yield its scores on a range of brain benchmarks and new benchmarks can be incorporated to challenge the models.

This quantified approach lets us keep track of how close our models are to the brain on a range of experiments (data) using different evaluation techniques (metrics). For more details, please refer to the technical paper and the perspective paper.

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Participate

Challenge the data: Submit a model

If you would like to score a model, please log in here.

Challenge the models: Submit data

If you have neural or behavioral recordings that you would like models to compete on, please get in touch with us to submit data.

Change the evaluation: Submit a metric

If you have an idea for a different way of comparing brain and machine, please send in a pull request.

Citation

If you use Brain-Score in your work, please cite Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? (technical) and Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence (perspective) as well as the respective benchmark sources.
@article{SchrimpfKubilius2018BrainScore,
  title={Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?},
  author={Martin Schrimpf and Jonas Kubilius and Ha Hong and Najib J. Majaj and Rishi Rajalingham and Elias B. Issa and Kohitij Kar and Pouya Bashivan and Jonathan Prescott-Roy and Franziska Geiger and Kailyn Schmidt and Daniel L. K. Yamins and James J. DiCarlo},
  journal={bioRxiv preprint},
  year={2018},
  url={https://www.biorxiv.org/content/10.1101/407007v2}
}

@article{Schrimpf2020integrative,
  title={Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence},
  author={Schrimpf, Martin and Kubilius, Jonas and Lee, Michael J and Murty, N Apurva Ratan and Ajemian, Robert and DiCarlo, James J},
  journal={Neuron},
  year={2020},
  url={https://www.cell.com/neuron/fulltext/S0896-6273(20)30605-X}
}